# Rethinking Legal Compliance Automation: Opportunities with Large Language Models

arXiv: https://arxiv.org/abs/2404.14356 · HTML: https://mlsanity.com/paper/2404.14356/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks](https://mlsanity.com/paper/2412.08593/) (2024)
- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)
- [Interpretation Gaps in LLM-Assisted Comprehension of Privacy Documents](https://mlsanity.com/paper/2503.12225/) (2025)
- [Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach](https://mlsanity.com/paper/2508.09935/) (2025)
- [From Regulation to Requirements: An Automated Requirement Derivation and Explanation Pipeline](https://mlsanity.com/paper/2607.04448/) (2026)
- [VeriWeave Govern: Evidence-Gated Deterministic Runtime Governance for Enterprise AI Agents](https://mlsanity.com/paper/2609.37457/) (2026)

## Abstract (arXiv)

As software-intensive systems face growing pressure to comply with laws and regulations, providing automated support for compliance analysis has become paramount. Despite advances in the Requirements Engineering (RE) community on legal compliance analysis, important obstacles remain in developing accurate and generalizable compliance automation solutions. This paper highlights some observed limitations of current approaches and examines how adopting new automation strategies that leverage Large Language Models (LLMs) can help address these shortcomings and open up fresh opportunities. Specifically, we argue that the examination of (textual) legal artifacts should, first, employ a broader context than sentences, which have widely been used as the units of analysis in past research. Second, the mode of analysis with legal artifacts needs to shift from classification and information extraction to more end-to-end strategies that are not only accurate but also capable of providing explanation and justification. We present a compliance analysis approach designed to address these limitations. We further outline our evaluation plan for the approach and provide preliminary evaluation results based on data processing agreements (DPAs) that must comply with the General Data Protection Regulation (GDPR). Our initial findings suggest that our approach yields substantial accuracy improvements and, at the same time, provides justification for compliance decisions.